Abstract
This paper introduces a forecasting procedure based on multivariate dynamic kernels to re-examine –under a non linear framework– the experimental tests reported by Welch and Goyal showing that several variables proposed in the academic literature are of no use to predict the equity premium under linear regressions. For this approach kernel functions for time series are used with multiple kernel learning in order to represent the relative importance of each of these variables.
Supported by MINECO project APCOM (TIN2014-57226-P) and Generalitat de Catalunya 2014 SGR 890 (MACDA).
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Notes
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Publicly available from http://www.hec.unil.ch/agoyal/.
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Fábregues, L., Arratia, A., Belanche, L.A. (2017). Forecasting Financial Time Series with Multiple Kernel Learning. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_16
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